CDC Dataset Purge: A Timeline of Public Health Data Removal

Analysis of CDC datasets backed up on archive.org during the Trump administration’s data purge

TidyTuesday
Data Visualization
R Programming
2025
A data visualization analysis exploring CDC dataset archival patterns from 2016 to 2025. The analysis reveals changing accessibility patterns and a significant increase in archival activity beginning in 2020, with a focus on public health data preservation.
Author

Steven Ponce

Published

February 8, 2025

Figure 1: A two-panel visualization of CDC dataset archival patterns. Top panel: Bar chart showing distribution of 1,257 datasets by access level - majority are either Unspecified (51.5%) or Public Access (47.9%). Bottom panel: Area chart showing cumulative growth of archived datasets from 2016 to 2025, with a notable increase in archival activity starting 2020, marked by a vertical dashed line.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    patchwork,      # The Composer of Plots
    camcorder       # Record Your Plot History 
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
tt <- tidytuesdayR::tt_load(2025, week = 06) 

cdc <- tt$cdc_datasets |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code
glimpse(cdc)
skim(cdc)

4. Tidy Data

Show code
# P1. Distribution plot  
distribution <- cdc |>
  mutate(
    access_category = case_when(
      public_access_level %in% c("public", "public domain") ~ "Public Access",
      public_access_level == "restricted public" ~ "Restricted Access",
      public_access_level == "non-public" ~ "No Public Access",
      TRUE ~ "Unspecified"
    )
  ) |>
  count(access_category) |>
  mutate(
    pct = n / sum(n) * 100,
    label = sprintf("%.1f%%\n(n=%d)", pct, n),
    # Conditional hjust and color based on count
    hjust = ifelse(n > 200, 1.2, -0.2),
    label_color = ifelse(n > 200, "white", "gray30")
  )

# P2. Timeline plot
timeline_data <- cdc |>
  mutate(
    access_category = case_when(
      public_access_level %in% c("public", "public domain") ~ "Public Access",
      public_access_level == "restricted public" ~ "Restricted Access",
      public_access_level == "non-public" ~ "No Public Access",
      TRUE ~ "Unspecified"
    ),
    issued = as.Date(issued)
  ) |>
  filter(!is.na(issued)) |>
  arrange(issued) |>
  group_by(access_category) |>
  mutate(cumulative = row_number()) |>
  ungroup()

# Find the inflection point around 2020
inflection_date <- as.Date("2020-04-01")

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
    palette = c(
        "Public Access"     = "#3498DB",      
        "Restricted Access" = "#E67E22",   
        "No Public Access"  = "#C0392B",    
        "Unspecified"       = "#7F8C8D" 
    )
)

### |-  titles and caption ----
title_text <- str_glue("CDC Dataset Purge: A Timeline of Public Health Data Removal")
subtitle_text <- str_glue("Analysis of CDC datasets backed up on archive.org during the Trump administration's data purge")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 06,
    source_text = "archive.org/details/20250128-cdc-datasets"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Axis elements
        axis.title = element_text(color = colors$text, size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(color = "grey80", linewidth = 0.1),
        
        # Legend elements
        legend.position = "right",
        legend.title = element_text(family = fonts$text, size = rel(0.8)),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # Plot margins 
        plot.margin = margin(t = 10, r = 10, b = 10, l = 10),

    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----

# P1. Distribution plot 
p1 <- ggplot(distribution,
             aes(x = reorder(access_category, n), 
                 y = n, 
                 fill = access_category)
) +
    
    # Geoms
    geom_col() +
    geom_text(
        aes(label = label,
            hjust = hjust,
            color = label_color),
        size = 3.5,
        fontface = "bold",
        family = fonts$text
    ) +
    coord_flip() +
    
    # Scales
    scale_fill_manual(values = colors$palette) +
    scale_color_identity() +  
    scale_x_discrete() +
    scale_y_continuous(
        expand = expansion(mult = c(0, 0.25)) 
    ) +
    
    # Labs
    labs(
        title = "Distribution of Dataset Access Levels",
        x = NULL,
        y = "Number of Datasets"
    ) +
    
    # Theme
    theme(
        legend.position = "none",
        panel.grid.major.y = element_blank(),
        
        plot.title = element_text(
            size   = rel(1.3),
            family = fonts$title,
            face   = "bold",
            color  = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        )
    )

# P2. Timeline plot with enhancements
p2 <- ggplot(timeline_data,
             aes(x = issued, 
                 y = cumulative, 
                 fill = access_category)
) +
    # Rectangle
    annotate(
        "rect",
        xmin = inflection_date,
        xmax = max(timeline_data$issued),
        ymin = 0,
        ymax = max(timeline_data$cumulative),
        fill = "gray85",
        alpha = 0.3
    ) +
    
    # Geoms
    geom_area(alpha = 0.8) +
    geom_line(color = "white", linewidth = 0.5) +
    geom_vline(
        xintercept = inflection_date, 
        linetype = "dashed", 
        color = colors$caption,
        alpha = 0.7
    ) +
    # Note
    annotate(
        "text", 
        x = inflection_date,
        y = max(timeline_data$cumulative) * 0.9,
        label = "Notable increase in\narchival activity",
        hjust = -0.1,
        size = 3.5,
        color = colors$caption,
        family = fonts$caption
    ) +
    
    # Scales
    scale_fill_manual(values = colors$palette) +
    scale_x_date(
        date_breaks = "1 year",
        date_labels = "%Y",
    ) +
    scale_y_continuous(
        expand = expansion(mult = c(0, 0.1)),
        breaks = function(x) seq(0, max(x), by = 50)
    ) +
    
    # Labs
    labs(
        title = "Cumulative Growth of Archived Datasets",
        subtitle = sprintf("Note: Only %d out of %d datasets (%.1f%%) have valid dates",
                           sum(!is.na(cdc$issued)),
                           nrow(cdc),
                           100 * sum(!is.na(cdc$issued)) / nrow(cdc)),
        x = "Year",
        y = "Number of Datasets"
    ) +
    
    # Theme
    theme(
        legend.position = "top",
        legend.title = element_blank(),
        legend.spacing.x = unit(0.2, "cm"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank(),
        
        plot.title = element_text(
            size = rel(1.3),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.9),
            family = fonts$caption,
            color = colors$caption,
            margin = margin(b = 10)
        ),
        axis.title = element_text(
            size = rel(0.9),
            family = fonts$caption,
            color = colors$caption
        ),
    )

# Combine plots 
combined_plot <- (p1 / p2) +
    plot_layout(heights = c(0.8, 1.2)) 

combined_plot <- combined_plot +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text, 
        theme = theme(
            plot.title = element_text(
                size   = rel(1.75),
                family = fonts$title,
                face   = "bold",
                color  = colors$title,
                lineheight = 1.1,
                margin = margin(t = 5, b = 5)
            ),   
            plot.subtitle = element_text(
                size   = rel(0.9),
                family = fonts$subtitle,
                color  = colors$subtitle,
                lineheight = 1.2,
                margin = margin(t = 5, b = 5)
            ), 
            plot.caption = element_markdown(
                size   = rel(0.6),
                family = fonts$caption,
                color  = colors$caption,
                hjust  = 0.5,
                margin = margin(t = 10)
            ),
            plot.margin = margin(t = 20, r = 10, b = 20, l = 10),
        )
    )

7. Save

Show code
### |-  plot image ----  

save_plot_patchwork(
  plot = combined_plot, 
  type = "tidytuesday", 
  year = 2025, 
  week = 6, 
  width = 10,
  height = 10
)

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 patchwork_1.3.0 here_1.0.1      glue_1.8.0     
 [5] scales_1.3.0    skimr_2.1.5     janitor_2.2.0   showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] repr_1.1.7         tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.3         
[29] rmarkdown_2.29     crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1 
[33] tzdb_0.4.0         commonmark_1.9.2   parallel_4.4.0     ggplotify_0.1.2   
[37] base64enc_0.1-3    vctrs_0.6.5        yulab.utils_0.1.8  jsonlite_1.8.9    
[41] gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2        systemfonts_1.1.0 
[45] magick_2.8.5       gifski_1.32.0-1    codetools_0.2-20   stringi_1.8.4     
[49] gtable_0.3.6       munsell_0.5.1      pillar_1.9.0       rappdirs_0.3.3    
[53] htmltools_0.5.8.1  R6_2.5.1           httr2_1.0.6        rprojroot_2.0.4   
[57] vroom_1.6.5        evaluate_1.0.1     markdown_1.13      gridtext_0.1.5    
[61] snakecase_0.11.1   renv_1.0.3         Rcpp_1.0.13-1      svglite_2.1.3     
[65] xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in tt_2025_06.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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